Sierra Simpson

Research Fellow, University of California, San Diego

7 active projects

Duplicate of How to Run Notebooks in the Background_Sierra

Some analyses take some time to run. Currently, the researcher has to wait for their job to run because if they are logged out of the system, their code will stop working and will not be executed. This is problematic…

Scientific Questions Being Studied

Some analyses take some time to run. Currently, the researcher has to wait for their job to run because if they are logged out of the system, their code will stop working and will not be executed. This is problematic for users working on datatypes such as Fitbit and Genomics.

To avoid this interruption, this notebook will run codes in the background.

Project Purpose(s)

  • Educational
  • Other Purpose (The notebook in this workspace shows how to run notebooks in the background even if the user is logged out of the workbench.)

Scientific Approaches

To run notebooks in the background, we use a special Python library called nbconvert. Users will specify the name of the notebook that they need to be executed. After that, they just need to run every cell in this notebook.

Anticipated Findings

There is no anticipated findings as this is for educational purpose only.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Sierra Simpson - Research Fellow, University of California, San Diego

Collaborators:

  • Rodney Gabriel - Early Career Tenure-track Researcher, University of California, San Diego

Rotation Project

I am using the dataset to explore the question of whether there is a genomic correlation between opioid use and chronic pain.

Scientific Questions Being Studied

I am using the dataset to explore the question of whether there is a genomic correlation between opioid use and chronic pain.

Project Purpose(s)

  • Educational

Scientific Approaches

I will use the controlled dataset to leverage the genomic data to perform GWAS on patients with a chronic pain diagnosis (denominator) and then compare those with opioid use disorder versus not. The GWAS will compare those two cohorts.

Anticipated Findings

I am hoping to identify some sequences that are common among patients with OUD and chronic pain versus those without OUD.

Demographic Categories of Interest

  • Race / Ethnicity
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Jun Qian - Other, All of Us Program Operational Use
  • Jennifer Zhang - Project Personnel, All of Us Program Operational Use

Rotation Project 2.0

I am using the dataset to explore the question of whether there is a genomic correlation between opioid use and chronic pain.

Scientific Questions Being Studied

I am using the dataset to explore the question of whether there is a genomic correlation between opioid use and chronic pain.

Project Purpose(s)

  • Educational

Scientific Approaches

I will use the controlled dataset to leverage the genomic data to perform GWAS on patients with a chronic pain diagnosis (denominator) and then compare those with opioid use disorder versus not. The GWAS will compare those two cohorts.

Anticipated Findings

I am hoping to identify some sequences that are common among patients with OUD and chronic pain versus those without OUD.

Demographic Categories of Interest

  • Race / Ethnicity
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Varshini Sathish - Graduate Trainee, University of California, San Diego
  • Sierra Simpson - Research Fellow, University of California, San Diego
  • Rodney Gabriel - Early Career Tenure-track Researcher, University of California, San Diego
  • Kathleen Fisch - Mid-career Tenured Researcher, University of California, San Diego
  • Hector Chavez - Project Personnel, University of California, San Diego

Hector's Rotation Project 2.0

I am using the dataset to explore the question of whether there is a genomic correlation between opioid use and chronic pain.

Scientific Questions Being Studied

I am using the dataset to explore the question of whether there is a genomic correlation between opioid use and chronic pain.

Project Purpose(s)

  • Educational

Scientific Approaches

I will use the controlled dataset to leverage the genomic data to perform GWAS on patients with a chronic pain diagnosis (denominator) and then compare those with opioid use disorder versus not. The GWAS will compare those two cohorts.

Anticipated Findings

I am hoping to identify some sequences that are common among patients with OUD and chronic pain versus those without OUD.

Demographic Categories of Interest

  • Race / Ethnicity
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Varshini Sathish - Graduate Trainee, University of California, San Diego
  • Sierra Simpson - Research Fellow, University of California, San Diego
  • Rodney Gabriel - Early Career Tenure-track Researcher, University of California, San Diego
  • Kathleen Fisch - Mid-career Tenured Researcher, University of California, San Diego
  • Hector Chavez - Project Personnel, University of California, San Diego

OUD + Chronic Pain

The intention of this study is to determine how genes, social , and clinical determinants play a role in persistent opioid use in groups with and without chronic pain. Patients experiencing chronic pain are more likely to become dependent with…

Scientific Questions Being Studied

The intention of this study is to determine how genes, social , and clinical determinants play a role in persistent opioid use in groups with and without chronic pain. Patients experiencing chronic pain are more likely to become dependent with up to 50% of patients on chronic opioid therapy meeting criteria to be diagnosed with OUD. Preventive measures focused on development of OUD is essential, yet biomarkers have been challenging to develop in a diverse cross section of patients, and clinical care is limited by resources. Thus, early identification of patients at risk for OUD may be impactful in reducing those at risk while allowing for the use of opioids in the clinic as an important pain mitigation tool. We aim to apply unbiased machine learning and artificial intelligence approaches to a wide range of data to better understand how clinicians can improve point of care and reduce OUD in diverse populations.

Project Purpose(s)

  • Disease Focused Research (opiate dependence)
  • Population Health
  • Social / Behavioral
  • Educational
  • Drug Development
  • Methods Development
  • Ancestry
  • Other Purpose (Research OUD in the context of a diverse dataset that incorporates clinical, social, and genomic features. )

Scientific Approaches

Precision medicine is an approach to disease treatment and prevention that takes into account individual factors. To effectively implement a precision medicine-based approach to preventing OUD, it is vital to develop tools that may identify patients early that are high risk for dependence. One approach would be to design predictive models for the development of OUD utilizing various individual data points, including genetics, social determinants, and clinical factors. Genomic research is transforming medicine by providing novel insights into disease biology and improving the diagnosis, prevention, and treatment of human disease. By identifying salient markers with the genome and combining that with social and clinical risk factors, it may be possible to better risk stratify patients for OUD. Early identification would lend well to a precision medicine approach, in which high risk patients may be provided with personalized care and management to prevent OUD.

Anticipated Findings

We expect to find distinct risk features in groups that experience chronic pain and those that do not in conjunction with social and clinical determinants. Understanding factors that play a role in the progression of OUD can help maximize clinical efforts for those that the highest risk despite any social, genomic, or clinical factors that may have obscured attention to those groups. We aim to expand the body of literature using a diverse dataset that takes into account equity and equality for all. Most previous OUD literature is in relatively homogenous groups of individuals and key features like gender have not been incorporated into outcome studies.

Demographic Categories of Interest

  • Race / Ethnicity
  • Gender Identity
  • Geography
  • Access to Care

Data Set Used

Registered Tier

Research Team

Owner:

Cancer

Outcomes following diagnosis of cancer are function of both biological and non-biological factors related to social determinants of health (SDoH). Up to 75% of cancer occurrences are associated with SDoH rather than clinical factors. While many studies have documented disparities…

Scientific Questions Being Studied

Outcomes following diagnosis of cancer are function of both biological and non-biological factors related to social determinants of health (SDoH). Up to 75% of cancer occurrences are associated with SDoH rather than clinical factors. While many studies have documented disparities in access to care and outcomes in cancer patients, an analysis of social and geological factors using state-of-the-art machine learning approaches and large-scale multi-institutional national data is needed. The AllOfUs dataset contains data from with granular SDoH data. Therefore, the objective of this study will be to leverage large clinical databases such as AllOfUs to characterize geographical patterns of access to care and quantify association of SDoH for outcomes in cancer patients.

Project Purpose(s)

  • Disease Focused Research (cancer)
  • Population Health
  • Social / Behavioral
  • Methods Development
  • Control Set

Scientific Approaches

Metric disparities will be characterized based on these geographic factors while controlling for clinical factors. Furthermore, a machine learning approach will be taken to assess the association of these geographic disparities with post-treatment outcomes, including major outcomes such recurrence and survival.

Anticipated Findings

Using state-of-the-art machine learning approaches, we will develop predictive models leveraging natural language processing for incidence of recurrence and survival in cancer patients and quantify the association of each SDoH variable on outcomes. The project will Identify disparities in access to care for cancer patients based on national geographic data and association with major post-treatment cancer recurrence and survival.

Demographic Categories of Interest

  • Geography
  • Access to Care

Data Set Used

Registered Tier

Research Team

Owner:

  • Sierra Simpson - Research Fellow, University of California, San Diego

Anosmia

Anosmia, or the loss of sense of smell, has been recognized as one of the common symptoms of COVID-19. Studying anosmia is important in the context of COVID-19 because it can aid in early detection and diagnosis of the disease,…

Scientific Questions Being Studied

Anosmia, or the loss of sense of smell, has been recognized as one of the common symptoms of COVID-19. Studying anosmia is important in the context of COVID-19 because it can aid in early detection and diagnosis of the disease, especially in asymptomatic patients. Moreover, anosmia can have significant implications for mental health outcomes as it has been associated with depression, anxiety, and a decrease in quality of life. Additionally, research has shown that loss of sense of smell can impact behavioral outcomes such as appetite and food intake. Understanding the impact of anosmia on behavioral and mental health outcomes is crucial in order to develop effective interventions and support for those affected. Therefore, further research on anosmia is essential in order to improve our understanding of COVID-19 and its associated impact on individuals' health and wellbeing.

Project Purpose(s)

  • Disease Focused Research (COVID-19)
  • Population Health

Scientific Approaches

We will use machine learning and artificial intelligence approaches to determine key features associated with those that suffer short and long term anosmia in both in the context of COVID and non-COVID indications.

Anticipated Findings

Research on anosmia and its relationship with COVID-19 can provide important insights into the prevalence and severity of this symptom in infected individuals. Understanding the potential long-term effects of anosmia on mental health and behavior can also inform the development of interventions to support affected individuals. Furthermore, investigations into the neural mechanisms underlying anosmia and its relationship with COVID-19 can provide new insights into the pathophysiology of the disease. Overall, these findings could contribute to the body of scientific knowledge in the field by improving our understanding of COVID-19 and its associated impact on health and wellbeing. Additionally, it could provide important information for the development of clinical guidelines and interventions for individuals with anosmia or COVID-19.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Sierra Simpson - Research Fellow, University of California, San Diego
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